In the digital age, individuals and organizations increasingly rely on digitally-stored data used for a variety of applications. For some purposes, a party may have cause to compare data owned by the party with data owned by another party, or to submit to such a comparison. For example, two parties may have cause to determine whether a data object owned by one party is similar to a data object owned by the other party. However, one or both parties may also have cause to maintain data privacy—not only from each other, but from all outside parties.
Previous attempts to simultaneously address the need to compare private data with the need to maintain data privacy have fallen short. For example, previously proposed similarity detection approaches that compute an exact similarity between two data objects may require computing resources that scale linearly with the size of the data objects. However, previously proposed approximate similarity detection procedures may allow a party that has submitted to such a procedure to leverage the approximation involve to manipulate submitted data in such a way as to preserve similarity while evading a detection of similarity by the procedure.
Accordingly, the instant disclosure identifies and addresses a need for additional and improved systems and methods for securely detecting data similarities.